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A study of carbon dioxide and methane in the global and regional (Siberia) scales: an overview Dmitry Belikov 1,* (dmitry.belikov@nies.go.jp), Shamil Maksyutov 2 , Alexander Starchenko 1 1. Tomsk State University, Tomsk, Russia; 2. National

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A study of carbon dioxide and methane in the global and regional (Siberia) scales: an

  • verview

Dmitry Belikov1,*(dmitry.belikov@nies.go.jp), Shamil Maksyutov2, Alexander Starchenko1

  • 1. Tomsk State University, Tomsk, Russia;
  • 2. National Institute for Environmental Studies, Tsukuba, Japan;

* Previously in National Institute of Polar Research, Tokyo, Japan;

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ENVIROMIS-2016

CO2 and CH4 concentrations

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Greenhouse gases and temperature

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IPCC report

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Global fossil fuel emission

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NBP of Russian terrestrial ecosystems

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Dolman et al., 2012

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Permafrost

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Permafrost

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Spatial and Temporal Scales

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Annual CO2 budget

Year-to-year variations Trend of CO2 balance Time scale Spatial scale Tower and aircraft

Seasonal change of CO2 flux

TCCON FTS Atmospheric models Chamber

Validation of each method

Discussion of trends in CO2 balance By Kumiko Takata Tree-ring analysis Satellite

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Chamber method observations

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Live biomass (LB, t/ha) and net primary production (NPP, t/ha/yr) distributions in west Siberian wetlands by Peregon et al., 2008

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Tree-ring analysis (Dendrochronology)

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www.geography.hunter.cuny.edu

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Tower and aircraft observations

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Tower observations

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Aggregated

Belikov et al., 2016

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Tower observations (Eddy covariance )

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Dolman et al., 2012

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Land Surface models

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An ensemble of 8 dynamic global vegetation models (DGVMs) for geographic Russia, Belarus and Ukraine by Dolman et al., 2012

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The Total Carbon Column Observing Network (TCCON) of a network of ground-based Fourier Transform Spectrometers (FTS)

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Satellite observations

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Atmospheric models

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Eulerian-Lagrangian coupled model

( )

2

ln n 1 1

1 , ,

IJ L N IJK N l B air r r ij ij ijk ijk ij l n ijk n CO

Tm C x t F f C f hNL m N

= = =

= +

Lagrangian (LPDM)

(FLEXPART 8.0)

Eulerian

(NIES-08.1i)

Ø treats the atmospheric tracers as a

continuum on a control volume basis;

Ø effective in reproducing of long-term

patterns, i.e. the seasonal cycle or interhemispheric gradient;

Ø running globally for long period. Ø considers atmospheric tracers as a

discrete phase and tracks each individual particle,

Ø better for resolving synoptic and hourly

variations;

Ø running back for every observation for

48h. Eulerian and Lagrangian components are coupled at the time boundary in the global domain. Ganshin et al., GMD 2011

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Atmospheric models (high-resolution)

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“Top-down” Flux Inversion Estimates

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by David Crisp

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“Top-down” Flux Inversion Estimates

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The aim of the inverse problem is to find the value

  • f a state vector x with n elements that minimizes a

cost function J(x) using a least-squares method:

Ø For linear forward transport model (H), Eq. can be

solved through an iterative minimization algorithm.

Ø The existence of the gradient of J(x) with respect

to x allows using of powerful gradient algorithms for minimization.

Ø This gradient is efficiently provided by the adjoint.

10.51 petaflops

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Data structure

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Multidisciplinary climate change study

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Multidisciplinary climate change study

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Multidisciplinary climate change study

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Comparison of top-down & bottom-up estimations

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✔ Monthly NEP (NBP) roughly agree among the methods & models!

  • Monthly NEP (Bottom-up models & Tower observation)

and Monthly NBP (Top-down models) Yakutsk (62N, 129E)

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Effect of including observation data in Siberia

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Summary

1.

There are various methods to study greenhouse gases emissions and concentrations;

2.

These methods have different spatial and time scales;

3.

Multidisciplinary projects are essential for collection and optimal use of various observation data;

4.

Siberia is target region for a multidisciplinary, multiscale and multicomponent research programs;

5.

Recent programs are aiming at resolving the major uncertainties in Earth system science and global sustainability issues concerning the Arctic and boreal pan-Eurasian regions.

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Acknowledgments

This study was performed by order of the Ministry for Education and Science of the Russian Federation No. 5.628.2014/K and was supported by The Tomsk State University Academic D.I. Mendeleev Fund Program in 2014–2015.

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